Overview

Dataset statistics

Number of variables19
Number of observations365788
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.4 MiB
Average record size in memory156.0 B

Variable types

Numeric15
Categorical4

Alerts

ADV_S is highly overall correlated with DATA_S and 3 other fieldsHigh correlation
DATA_R is highly overall correlated with Data_Sent_To_BS and 5 other fieldsHigh correlation
DATA_S is highly overall correlated with ADV_S and 3 other fieldsHigh correlation
Data_Sent_To_BS is highly overall correlated with DATA_R and 3 other fieldsHigh correlation
Dist_To_CH is highly overall correlated with DATA_R and 3 other fieldsHigh correlation
Is_CH is highly overall correlated with JOIN_R and 6 other fieldsHigh correlation
JOIN_R is highly overall correlated with ADV_S and 2 other fieldsHigh correlation
JOIN_S is highly overall correlated with DATA_R and 7 other fieldsHigh correlation
SCH_R is highly overall correlated with DATA_R and 7 other fieldsHigh correlation
SCH_S is highly overall correlated with ADV_S and 2 other fieldsHigh correlation
Time is highly overall correlated with id and 1 other fieldsHigh correlation
dist_CH_To_BS is highly overall correlated with DATA_R and 5 other fieldsHigh correlation
id is highly overall correlated with Time and 1 other fieldsHigh correlation
send_code is highly overall correlated with DATA_R and 6 other fieldsHigh correlation
who_CH is highly overall correlated with Time and 1 other fieldsHigh correlation
Attack_type is highly overall correlated with Is_CH and 2 other fieldsHigh correlation
Rank is highly overall correlated with ADV_S and 4 other fieldsHigh correlation
Attack_type is highly imbalanced (73.6%)Imbalance
Expaned_Energy is highly skewed (γ1 = 25.69934767)Skewed
Dist_To_CH has 77990 (21.3%) zerosZeros
ADV_S has 323400 (88.4%) zerosZeros
ADV_R has 30155 (8.2%) zerosZeros
JOIN_R has 347118 (94.9%) zerosZeros
SCH_S has 347130 (94.9%) zerosZeros
Rank has 70737 (19.3%) zerosZeros
DATA_S has 58829 (16.1%) zerosZeros
DATA_R has 314028 (85.8%) zerosZeros
Data_Sent_To_BS has 303545 (83.0%) zerosZeros
dist_CH_To_BS has 303545 (83.0%) zerosZeros
send_code has 77949 (21.3%) zerosZeros

Reproduction

Analysis started2024-05-17 12:16:13.467037
Analysis finished2024-05-17 12:17:25.501319
Duration1 minute and 12.03 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION 

Distinct11120
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265027.89
Minimum101000
Maximum3402096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:25.682814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile102035
Q1107096
median116072
Q3214072
95-th percentile804098
Maximum3402096
Range3301096
Interquartile range (IQR)106976

Descriptive statistics

Standard deviation363296.1
Coefficient of variation (CV)1.3707844
Kurtosis23.490804
Mean265027.89
Median Absolute Deviation (MAD)13047
Skewness4.3864773
Sum9.6944023 × 1010
Variance1.3198406 × 1011
MonotonicityNot monotonic
2024-05-17T17:47:25.912201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101041 136
 
< 0.1%
101000 135
 
< 0.1%
101065 135
 
< 0.1%
101075 135
 
< 0.1%
101074 135
 
< 0.1%
101073 135
 
< 0.1%
101072 135
 
< 0.1%
101071 135
 
< 0.1%
101070 135
 
< 0.1%
101069 135
 
< 0.1%
Other values (11110) 364437
99.6%
ValueCountFrequency (%)
101000 135
< 0.1%
101001 135
< 0.1%
101002 135
< 0.1%
101003 135
< 0.1%
101004 135
< 0.1%
101005 135
< 0.1%
101006 135
< 0.1%
101007 135
< 0.1%
101008 135
< 0.1%
101009 135
< 0.1%
ValueCountFrequency (%)
3402096 1
< 0.1%
3402088 1
< 0.1%
3402073 1
< 0.1%
3402069 1
< 0.1%
3402063 1
< 0.1%
3402054 1
< 0.1%
3402034 1
< 0.1%
3402016 1
< 0.1%
3402009 1
< 0.1%
3402001 1
< 0.1%

Time
Real number (ℝ)

HIGH CORRELATION 

Distinct196
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1062.5201
Minimum50
Maximum3600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:26.135603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile103
Q1353
median803
Q31503
95-th percentile3053
Maximum3600
Range3550
Interquartile range (IQR)1150

Descriptive statistics

Standard deviation896.35523
Coefficient of variation (CV)0.84361247
Kurtosis0.30756442
Mean1062.5201
Median Absolute Deviation (MAD)500
Skewness1.1037821
Sum3.8865711 × 108
Variance803452.69
MonotonicityNot monotonic
2024-05-17T17:47:26.353047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 13201
 
3.6%
103 13101
 
3.6%
253 13065
 
3.6%
153 12805
 
3.5%
203 12701
 
3.5%
303 12649
 
3.5%
353 12356
 
3.4%
403 11702
 
3.2%
453 11297
 
3.1%
503 11177
 
3.1%
Other values (186) 241734
66.1%
ValueCountFrequency (%)
50 300
 
0.1%
53 13201
3.6%
100 300
 
0.1%
103 13101
3.6%
150 300
 
0.1%
153 12805
3.5%
200 300
 
0.1%
203 12701
3.5%
250 300
 
0.1%
253 13065
3.6%
ValueCountFrequency (%)
3600 10
 
< 0.1%
3553 1687
0.5%
3550 10
 
< 0.1%
3503 1738
0.5%
3500 10
 
< 0.1%
3473 1
 
< 0.1%
3453 1728
0.5%
3450 10
 
< 0.1%
3403 1772
0.5%
3400 10
 
< 0.1%

Is_CH
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
0
323400 
1
42388 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Length

2024-05-17T17:47:26.553483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T17:47:26.719042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 365788
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 365788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 323400
88.4%
1 42388
 
11.6%

who_CH
Real number (ℝ)

HIGH CORRELATION 

Distinct7088
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265038.68
Minimum101000
Maximum3402100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:26.894601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile102034
Q1107100
median116073
Q3214089
95-th percentile804100
Maximum3402100
Range3301100
Interquartile range (IQR)106989

Descriptive statistics

Standard deviation363308.73
Coefficient of variation (CV)1.3707762
Kurtosis23.489083
Mean265038.68
Median Absolute Deviation (MAD)13048
Skewness4.3863001
Sum9.694797 × 1010
Variance1.3199323 × 1011
MonotonicityNot monotonic
2024-05-17T17:47:27.118003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202100 3422
 
0.9%
201100 1854
 
0.5%
301100 1751
 
0.5%
203100 1599
 
0.4%
501100 1519
 
0.4%
601100 1475
 
0.4%
402100 1474
 
0.4%
502100 1456
 
0.4%
401100 1449
 
0.4%
602100 1415
 
0.4%
Other values (7078) 348374
95.2%
ValueCountFrequency (%)
101000 196
0.1%
101001 374
0.1%
101002 104
 
< 0.1%
101003 277
0.1%
101005 293
0.1%
101006 255
0.1%
101007 134
 
< 0.1%
101008 78
 
< 0.1%
101009 185
0.1%
101010 93
 
< 0.1%
ValueCountFrequency (%)
3402100 10
 
< 0.1%
3401100 26
 
< 0.1%
3302100 58
< 0.1%
3301100 81
< 0.1%
3301096 1
 
< 0.1%
3301054 1
 
< 0.1%
3301034 1
 
< 0.1%
3301001 1
 
< 0.1%
3202100 84
< 0.1%
3202096 1
 
< 0.1%

Dist_To_CH
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13956
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.786868
Minimum0
Maximum214.27462
Zeros77990
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:27.342401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.74685
median18.56966
Q333.9213
95-th percentile62.54398
Maximum214.27462
Range214.27462
Interquartile range (IQR)28.17445

Descriptive statistics

Standard deviation21.944243
Coefficient of variation (CV)0.96302145
Kurtosis5.5162019
Mean22.786868
Median Absolute Deviation (MAD)14.28935
Skewness1.6674018
Sum8335162.9
Variance481.54979
MonotonicityNot monotonic
2024-05-17T17:47:27.542864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77990
 
21.3%
5.96867 357
 
0.1%
8.75269 298
 
0.1%
8.97549 292
 
0.1%
3.96705 273
 
0.1%
6.555 271
 
0.1%
8.67263 266
 
0.1%
1.1225 266
 
0.1%
3.07338 260
 
0.1%
7.04221 256
 
0.1%
Other values (13946) 285259
78.0%
ValueCountFrequency (%)
0 77990
21.3%
0.40092 126
 
< 0.1%
0.48993 81
 
< 0.1%
0.66713 85
 
< 0.1%
0.67335 95
 
< 0.1%
0.80238 43
 
< 0.1%
0.83439 109
 
< 0.1%
0.88798 251
 
0.1%
1.1225 266
 
0.1%
1.23076 237
 
0.1%
ValueCountFrequency (%)
214.27462 25
< 0.1%
213.85247 20
< 0.1%
206.32935 9
 
< 0.1%
205.22083 9
 
< 0.1%
203.79135 8
 
< 0.1%
202.98739 7
 
< 0.1%
190.43411 5
 
< 0.1%
186.94848 7
 
< 0.1%
184.31794 6
 
< 0.1%
183.55017 7
 
< 0.1%

ADV_S
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26713014
Minimum0
Maximum97
Zeros323400
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:27.751307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0703395
Coefficient of variation (CV)7.7503029
Kurtosis547.0163
Mean0.26713014
Median Absolute Deviation (MAD)0
Skewness19.543366
Sum97713
Variance4.2863057
MonotonicityNot monotonic
2024-05-17T17:47:27.977673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 323400
88.4%
1 38884
 
10.6%
6 194
 
0.1%
10 188
 
0.1%
17 184
 
0.1%
18 183
 
0.1%
16 177
 
< 0.1%
13 175
 
< 0.1%
11 175
 
< 0.1%
19 174
 
< 0.1%
Other values (75) 2054
 
0.6%
ValueCountFrequency (%)
0 323400
88.4%
1 38884
 
10.6%
3 121
 
< 0.1%
4 119
 
< 0.1%
5 121
 
< 0.1%
6 194
 
0.1%
7 111
 
< 0.1%
8 116
 
< 0.1%
9 103
 
< 0.1%
10 188
 
0.1%
ValueCountFrequency (%)
97 2
 
< 0.1%
96 3
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 1
 
< 0.1%
88 2
 
< 0.1%
87 1
 
< 0.1%
84 1
 
< 0.1%
83 5
< 0.1%

ADV_R
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9806719
Minimum0
Maximum117
Zeros30155
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:28.170185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q37
95-th percentile27
Maximum117
Range117
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.0370171
Coefficient of variation (CV)1.0080716
Kurtosis8.5009254
Mean6.9806719
Median Absolute Deviation (MAD)2
Skewness2.3047779
Sum2553446
Variance49.519609
MonotonicityNot monotonic
2024-05-17T17:47:28.372617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 53069
14.5%
5 51475
14.1%
6 42430
11.6%
3 37532
10.3%
7 34155
9.3%
0 30155
8.2%
2 23314
6.4%
8 20640
 
5.6%
9 13413
 
3.7%
28 10860
 
3.0%
Other values (21) 48745
13.3%
ValueCountFrequency (%)
0 30155
8.2%
1 8077
 
2.2%
2 23314
6.4%
3 37532
10.3%
4 53069
14.5%
5 51475
14.1%
6 42430
11.6%
7 34155
9.3%
8 20640
 
5.6%
9 13413
 
3.7%
ValueCountFrequency (%)
117 34
 
< 0.1%
29 7
 
< 0.1%
28 10860
3.0%
27 7732
2.1%
26 7877
2.2%
25 4517
1.2%
24 2051
 
0.6%
23 894
 
0.2%
22 729
 
0.2%
21 814
 
0.2%

JOIN_S
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
1
287839 
0
77949 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Length

2024-05-17T17:47:28.596049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T17:47:28.750635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring characters

ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 365788
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 365788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 287839
78.7%
0 77949
 
21.3%

JOIN_R
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74639682
Minimum0
Maximum124
Zeros347118
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:28.928130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum124
Range124
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7140532
Coefficient of variation (CV)6.3157466
Kurtosis123.06007
Mean0.74639682
Median Absolute Deviation (MAD)0
Skewness9.7145762
Sum273023
Variance22.222297
MonotonicityNot monotonic
2024-05-17T17:47:29.161533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347118
94.9%
1 1745
 
0.5%
2 1278
 
0.3%
3 1159
 
0.3%
4 1033
 
0.3%
5 935
 
0.3%
6 869
 
0.2%
7 709
 
0.2%
8 692
 
0.2%
9 657
 
0.2%
Other values (91) 9593
 
2.6%
ValueCountFrequency (%)
0 347118
94.9%
1 1745
 
0.5%
2 1278
 
0.3%
3 1159
 
0.3%
4 1033
 
0.3%
5 935
 
0.3%
6 869
 
0.2%
7 709
 
0.2%
8 692
 
0.2%
9 657
 
0.2%
ValueCountFrequency (%)
124 1
 
< 0.1%
99 47
< 0.1%
98 9
 
< 0.1%
97 4
 
< 0.1%
96 2
 
< 0.1%
95 4
 
< 0.1%
94 4
 
< 0.1%
93 5
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%

SCH_S
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29505615
Minimum0
Maximum99
Zeros347130
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:29.380918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7854019
Coefficient of variation (CV)9.4402434
Kurtosis320.82861
Mean0.29505615
Median Absolute Deviation (MAD)0
Skewness15.734658
Sum107928
Variance7.7584637
MonotonicityNot monotonic
2024-05-17T17:47:29.611330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347130
94.9%
1 12909
 
3.5%
2 366
 
0.1%
4 350
 
0.1%
3 339
 
0.1%
6 304
 
0.1%
5 292
 
0.1%
8 233
 
0.1%
9 228
 
0.1%
10 222
 
0.1%
Other values (85) 3415
 
0.9%
ValueCountFrequency (%)
0 347130
94.9%
1 12909
 
3.5%
2 366
 
0.1%
3 339
 
0.1%
4 350
 
0.1%
5 292
 
0.1%
6 304
 
0.1%
7 219
 
0.1%
8 233
 
0.1%
9 228
 
0.1%
ValueCountFrequency (%)
99 10
< 0.1%
98 3
 
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 3
 
< 0.1%
92 1
 
< 0.1%
90 2
 
< 0.1%
89 4
 
< 0.1%
88 1
 
< 0.1%

SCH_R
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
1
276081 
0
89707 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Length

2024-05-17T17:47:29.813789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T17:47:29.973362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring characters

ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 365788
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common 365788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 276081
75.5%
0 89707
 
24.5%

Rank
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7072567
Minimum0
Maximum99
Zeros70737
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:30.156869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q313
95-th percentile41
Maximum99
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.644887
Coefficient of variation (CV)1.5086535
Kurtosis6.7339523
Mean9.7072567
Median Absolute Deviation (MAD)3
Skewness2.3951015
Sum3550798
Variance214.4727
MonotonicityNot monotonic
2024-05-17T17:47:30.374289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 95159
26.0%
0 70737
19.3%
3 13506
 
3.7%
2 12797
 
3.5%
5 11285
 
3.1%
4 10533
 
2.9%
7 9693
 
2.6%
6 9009
 
2.5%
9 8567
 
2.3%
8 7746
 
2.1%
Other values (90) 116756
31.9%
ValueCountFrequency (%)
0 70737
19.3%
1 95159
26.0%
2 12797
 
3.5%
3 13506
 
3.7%
4 10533
 
2.9%
5 11285
 
3.1%
6 9009
 
2.5%
7 9693
 
2.6%
8 7746
 
2.1%
9 8567
 
2.3%
ValueCountFrequency (%)
99 39
< 0.1%
98 49
< 0.1%
97 61
< 0.1%
96 50
< 0.1%
95 63
< 0.1%
94 63
< 0.1%
93 78
< 0.1%
92 73
< 0.1%
91 80
< 0.1%
90 80
< 0.1%

DATA_S
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.274334
Minimum0
Maximum241
Zeros58829
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:30.590712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median36
Q362
95-th percentile131
Maximum241
Range241
Interquartile range (IQR)49

Descriptive statistics

Standard deviation42.675699
Coefficient of variation (CV)0.94260248
Kurtosis3.0435337
Mean45.274334
Median Absolute Deviation (MAD)23
Skewness1.5520597
Sum16560808
Variance1821.2153
MonotonicityNot monotonic
2024-05-17T17:47:30.825056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58829
 
16.1%
13 36422
 
10.0%
85 7355
 
2.0%
55 6899
 
1.9%
65 6797
 
1.9%
76 6754
 
1.8%
62 6679
 
1.8%
72 6677
 
1.8%
57 6408
 
1.8%
80 6385
 
1.7%
Other values (182) 216583
59.2%
ValueCountFrequency (%)
0 58829
16.1%
1 17
 
< 0.1%
2 259
 
0.1%
3 304
 
0.1%
4 375
 
0.1%
5 503
 
0.1%
6 558
 
0.2%
7 480
 
0.1%
8 440
 
0.1%
9 363
 
0.1%
ValueCountFrequency (%)
241 1641
0.4%
240 1
 
< 0.1%
237 1
 
< 0.1%
235 1
 
< 0.1%
229 2
 
< 0.1%
226 1
 
< 0.1%
220 1
 
< 0.1%
206 2429
0.7%
205 1
 
< 0.1%
204 1
 
< 0.1%

DATA_R
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1345
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.145751
Minimum0
Maximum1496
Zeros314028
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:31.045466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile609
Maximum1496
Range1496
Interquartile range (IQR)0

Descriptive statistics

Standard deviation228.64922
Coefficient of variation (CV)3.169268
Kurtosis13.054269
Mean72.145751
Median Absolute Deviation (MAD)0
Skewness3.6474665
Sum26390050
Variance52280.465
MonotonicityNot monotonic
2024-05-17T17:47:31.270863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 314028
85.8%
117 5999
 
1.6%
351 3217
 
0.9%
377 2772
 
0.8%
91 1647
 
0.5%
241 1429
 
0.4%
78 1342
 
0.4%
104 1293
 
0.4%
611 903
 
0.2%
412 724
 
0.2%
Other values (1335) 32434
 
8.9%
ValueCountFrequency (%)
0 314028
85.8%
1 140
 
< 0.1%
2 5
 
< 0.1%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
1496 1
 
< 0.1%
1494 4
 
< 0.1%
1493 2
 
< 0.1%
1492 10
< 0.1%
1491 6
< 0.1%
1490 1
 
< 0.1%
1489 3
 
< 0.1%
1488 2
 
< 0.1%
1487 5
< 0.1%
1486 5
< 0.1%

Data_Sent_To_BS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct237
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4941059
Minimum0
Maximum241
Zeros303545
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:31.493294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum241
Range241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.730293
Coefficient of variation (CV)4.3902599
Kurtosis72.090852
Mean4.4941059
Median Absolute Deviation (MAD)0
Skewness7.8497284
Sum1643890
Variance389.28446
MonotonicityNot monotonic
2024-05-17T17:47:31.716700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 303545
83.0%
13 38186
 
10.4%
1 2866
 
0.8%
2 1769
 
0.5%
3 1113
 
0.3%
6 764
 
0.2%
4 761
 
0.2%
5 729
 
0.2%
8 696
 
0.2%
7 635
 
0.2%
Other values (227) 14724
 
4.0%
ValueCountFrequency (%)
0 303545
83.0%
1 2866
 
0.8%
2 1769
 
0.5%
3 1113
 
0.3%
4 761
 
0.2%
5 729
 
0.2%
6 764
 
0.2%
7 635
 
0.2%
8 696
 
0.2%
9 571
 
0.2%
ValueCountFrequency (%)
241 447
0.1%
240 85
 
< 0.1%
239 42
 
< 0.1%
238 32
 
< 0.1%
237 13
 
< 0.1%
236 15
 
< 0.1%
235 14
 
< 0.1%
234 9
 
< 0.1%
233 4
 
< 0.1%
232 12
 
< 0.1%

dist_CH_To_BS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.580986
Minimum0
Maximum201.93494
Zeros303545
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:32.526502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile145.08942
Maximum201.93494
Range201.93494
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49.306364
Coefficient of variation (CV)2.2847132
Kurtosis2.4268386
Mean21.580986
Median Absolute Deviation (MAD)0
Skewness2.0069991
Sum7894065.8
Variance2431.1175
MonotonicityNot monotonic
2024-05-17T17:47:32.765862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 303545
83.0%
136.03878 1454
 
0.4%
159.31297 1446
 
0.4%
88.47785 1201
 
0.3%
93.93772 1131
 
0.3%
165.46205 1105
 
0.3%
123.96292 1031
 
0.3%
102.66424 995
 
0.3%
201.93494 983
 
0.3%
54.93262 957
 
0.3%
Other values (295) 51940
 
14.2%
ValueCountFrequency (%)
0 303545
83.0%
54.93262 957
 
0.3%
75.52972 235
 
0.1%
76.04676 96
 
< 0.1%
77.63787 76
 
< 0.1%
77.82286 61
 
< 0.1%
78.70375 64
 
< 0.1%
78.91449 223
 
0.1%
79.19069 228
 
0.1%
79.55137 82
 
< 0.1%
ValueCountFrequency (%)
201.93494 983
0.3%
181.31284 948
0.3%
176.98899 75
 
< 0.1%
176.62353 224
 
0.1%
176.42103 58
 
< 0.1%
176.40744 512
0.1%
176.34359 62
 
< 0.1%
175.01596 69
 
< 0.1%
174.29523 63
 
< 0.1%
173.64043 225
 
0.1%

send_code
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5156402
Minimum0
Maximum15
Zeros77949
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:32.962363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.397266
Coefficient of variation (CV)0.95294469
Kurtosis3.0981937
Mean2.5156402
Median Absolute Deviation (MAD)1
Skewness1.4483411
Sum920191
Variance5.7468842
MonotonicityNot monotonic
2024-05-17T17:47:33.139862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 77949
21.3%
1 74193
20.3%
2 62069
17.0%
3 49542
13.5%
4 37423
10.2%
5 25480
 
7.0%
6 15731
 
4.3%
7 10147
 
2.8%
8 5095
 
1.4%
9 2531
 
0.7%
Other values (6) 5628
 
1.5%
ValueCountFrequency (%)
0 77949
21.3%
1 74193
20.3%
2 62069
17.0%
3 49542
13.5%
4 37423
10.2%
5 25480
 
7.0%
6 15731
 
4.3%
7 10147
 
2.8%
8 5095
 
1.4%
9 2531
 
0.7%
ValueCountFrequency (%)
15 595
 
0.2%
14 628
 
0.2%
13 884
 
0.2%
12 836
 
0.2%
11 1097
 
0.3%
10 1588
 
0.4%
9 2531
 
0.7%
8 5095
 
1.4%
7 10147
2.8%
6 15731
4.3%

Expaned_Energy
Real number (ℝ)

SKEWED 

Distinct69352
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30334516
Minimum0
Maximum45.09394
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-05-17T17:47:33.353318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00739
Q10.05598
median0.09755
Q30.21703
95-th percentile1.6656
Maximum45.09394
Range45.09394
Interquartile range (IQR)0.16105

Descriptive statistics

Standard deviation0.67209017
Coefficient of variation (CV)2.2155955
Kurtosis1613.5145
Mean0.30334516
Median Absolute Deviation (MAD)0.053175
Skewness25.699348
Sum110960.02
Variance0.45170519
MonotonicityNot monotonic
2024-05-17T17:47:33.561733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00447 1201
 
0.3%
0.00448 874
 
0.2%
0.00446 657
 
0.2%
0.00606 404
 
0.1%
0.2194 390
 
0.1%
0.00607 382
 
0.1%
0.00722 364
 
0.1%
0.00723 351
 
0.1%
0.00449 332
 
0.1%
0.21826 323
 
0.1%
Other values (69342) 360510
98.6%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.00093 8
 
< 0.1%
0.00108 62
< 0.1%
0.00167 1
 
< 0.1%
0.00168 15
 
< 0.1%
0.00169 27
< 0.1%
0.0017 12
 
< 0.1%
0.00171 13
 
< 0.1%
0.00172 23
 
< 0.1%
0.00173 22
 
< 0.1%
ValueCountFrequency (%)
45.09394 1
< 0.1%
45.09063 1
< 0.1%
45.07812 1
< 0.1%
45.07668 1
< 0.1%
45.07618 1
< 0.1%
45.07489 1
< 0.1%
45.0745 1
< 0.1%
45.07431 1
< 0.1%
45.07407 1
< 0.1%
45.07405 1
< 0.1%

Attack_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
3
332040 
2
 
13909
0
 
10049
4
 
6633
1
 
3157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters365788
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 332040
90.8%
2 13909
 
3.8%
0 10049
 
2.7%
4 6633
 
1.8%
1 3157
 
0.9%

Length

2024-05-17T17:47:33.759206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T17:47:33.929749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 332040
90.8%
2 13909
 
3.8%
0 10049
 
2.7%
4 6633
 
1.8%
1 3157
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 332040
90.8%
2 13909
 
3.8%
0 10049
 
2.7%
4 6633
 
1.8%
1 3157
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 365788
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 332040
90.8%
2 13909
 
3.8%
0 10049
 
2.7%
4 6633
 
1.8%
1 3157
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 365788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 332040
90.8%
2 13909
 
3.8%
0 10049
 
2.7%
4 6633
 
1.8%
1 3157
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 332040
90.8%
2 13909
 
3.8%
0 10049
 
2.7%
4 6633
 
1.8%
1 3157
 
0.9%

Interactions

2024-05-17T17:47:19.685887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:30.912361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:34.566580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:38.003412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:41.608765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:44.965757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:48.363665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:51.909205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:55.325068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:58.692031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:02.235549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:05.967592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:09.342561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:12.805296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:16.229107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:19.929238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:31.392076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:34.813919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:38.238755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:41.842113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:45.203123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:48.600033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:52.161502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:55.562405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:58.928399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:02.458951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:06.203958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:09.565938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:13.045653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:16.465477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:20.150615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:31.631463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:35.046295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:38.482132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:42.073522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:45.436524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:48.839393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:52.397898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:55.799797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:59.163795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:02.693325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:06.431352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:09.796346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:13.289002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:16.711818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:20.384987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:31.861818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:35.288678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:38.722459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:42.296924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:45.673861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:49.073764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:52.630276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:56.032174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:59.403127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:02.919718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:06.660739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:10.028698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:13.519359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:16.949208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:20.584456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:32.091204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:35.510055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:38.936886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:42.506338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:45.899257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:49.284229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:52.851655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:56.249564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:59.628552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:03.130183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:06.875163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:10.240131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:13.739768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:17.165632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:20.799878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:32.315631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:35.727474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:39.156327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:42.722756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:46.116705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:49.497658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:53.071069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:56.472968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:59.846967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:03.347601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:07.096544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:10.455557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:13.966191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:17.381053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:21.011340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:32.537037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:35.959853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:39.381696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:42.955135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:46.344068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:49.736020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:53.301482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:56.695372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:00.077326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:03.568981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:07.319972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:10.676964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:14.191560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:17.612436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:21.225738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:32.772380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:36.189239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:39.614076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:43.187513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:46.563509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:49.956403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:53.529872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:56.908800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:00.297733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:03.791414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:07.539387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:10.902362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:14.420976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:17.845781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:21.433211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:32.987831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:36.406684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:39.840497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:43.398949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:46.779901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:50.178808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:53.754242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:57.121232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:00.524155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:04.015813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:07.760767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:11.117812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:14.642355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:18.074199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:21.669554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:33.204253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:36.627066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:40.068861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:43.622377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:46.995355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:50.400243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:53.970690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:57.335688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:00.740576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:04.226222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:07.979208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:11.398036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:14.863760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:18.298572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:21.984710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:33.420648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:36.849473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:40.416929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:43.835778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:47.210779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:50.717368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:54.194064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:57.556097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:01.017807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:04.441648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:08.197598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:11.679280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:15.079214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:18.526962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:22.203154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:33.667014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:37.085866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:40.668284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:44.062173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:47.454125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:50.994627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:54.421456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:57.795429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:01.310025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:04.680008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:08.423993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:11.914652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:15.312559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:18.759366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:22.409599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:33.885430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:37.302262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:40.889689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:44.301564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:47.676533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:51.221021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:54.639874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:58.012846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:01.530437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:04.914409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:08.642434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:12.126114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:15.531003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:19.002715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:22.640953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:34.124789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:37.542620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:41.138025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:44.532914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:47.915862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:51.456389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:54.883220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:58.247247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:01.781764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:05.151745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:08.881768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:12.362455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:15.773327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:19.250025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:22.865352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:34.349191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:37.781007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:41.370377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:44.755348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:48.145248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:51.692787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:55.111637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:46:58.472645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:02.019158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:05.759152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:09.113146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:12.589873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:16.008725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T17:47:19.470463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-17T17:47:34.077381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ADV_RADV_SDATA_RDATA_SData_Sent_To_BSDist_To_CHIs_CHJOIN_RJOIN_SSCH_RSCH_STimedist_CH_To_BSidsend_codewho_CHAttack_typeExpaned_EnergyRank
ADV_R1.0000.277-0.3210.177-0.232-0.0580.4270.0080.2550.3730.010-0.052-0.227-0.0540.309-0.0530.3100.071-0.023
ADV_S0.2771.0000.217-0.5270.343-0.4950.2350.6350.1630.1070.6360.2340.3330.263-0.5000.2630.419-0.047-0.512
DATA_R-0.3210.2171.000-0.4610.783-0.5240.3240.4180.6210.5610.4120.2560.7660.301-0.5430.3030.1220.450-0.426
DATA_S0.177-0.527-0.4611.000-0.4580.2430.468-0.3380.6730.737-0.338-0.115-0.460-0.1670.601-0.1680.2030.2480.231
Data_Sent_To_BS-0.2320.3430.783-0.4581.000-0.5570.3830.2620.2580.2320.2580.3220.9900.387-0.5560.3890.1410.469-0.475
Dist_To_CH-0.058-0.495-0.5240.243-0.5571.0000.324-0.3170.4660.417-0.317-0.374-0.558-0.4150.426-0.4170.140-0.3530.558
Is_CH0.4270.2350.3240.4680.3830.3241.0000.6400.6960.6130.6400.2370.3290.264-0.5000.2640.868-0.052-0.512
JOIN_R0.0080.6350.418-0.3380.262-0.3170.6401.0000.2820.2580.9990.0190.2190.011-0.3200.0110.2120.086-0.328
JOIN_S0.2550.1630.6210.6730.2580.4660.6960.2821.0000.896-0.445-0.432-0.778-0.4930.719-0.4950.604-0.2450.647
SCH_R0.3730.1070.5610.7370.2320.4170.6130.2580.8961.000-0.407-0.369-0.693-0.4340.621-0.4360.532-0.2280.562
SCH_S0.0100.6360.412-0.3380.258-0.3170.6400.999-0.445-0.4071.0000.0220.2160.014-0.3200.0140.3570.082-0.328
Time-0.0520.2340.256-0.1150.322-0.3740.2370.019-0.432-0.3690.0221.0000.3320.983-0.2800.9830.1950.169-0.454
dist_CH_To_BS-0.2270.3330.766-0.4600.990-0.5580.3290.219-0.778-0.6930.2160.3321.0000.399-0.5580.4000.1710.452-0.470
id-0.0540.2630.301-0.1670.387-0.4150.2640.011-0.493-0.4340.0140.9830.3991.000-0.3230.9990.2140.199-0.492
send_code0.309-0.500-0.5430.601-0.5560.426-0.500-0.3200.7190.621-0.320-0.280-0.558-0.3231.000-0.3240.186-0.0410.397
who_CH-0.0530.2630.303-0.1680.389-0.4170.2640.011-0.495-0.4360.0140.9830.4000.999-0.3241.0000.2140.201-0.493
Attack_type0.3100.4190.1220.2030.1410.1400.8680.2120.6040.5320.3570.1950.1710.2140.1860.2141.0000.1130.281
Expaned_Energy0.071-0.0470.4500.2480.469-0.353-0.0520.086-0.245-0.2280.0820.1690.4520.199-0.0410.2010.1131.000-0.281
Rank-0.023-0.512-0.4260.231-0.4750.558-0.512-0.3280.6470.562-0.328-0.454-0.470-0.4920.397-0.4930.281-0.2811.000

Missing values

2024-05-17T17:47:23.101720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-17T17:47:24.278570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idTimeIs_CHwho_CHDist_To_CHADV_SADV_RJOIN_SJOIN_RSCH_SSCH_RRankDATA_SDATA_RData_Sent_To_BSdist_CH_To_BSsend_codeExpaned_EnergyAttack_type
01010005011010000.00000100251000120048130.0853502.469403
110100150010104475.32345041001238000.0000040.069573
210100250010101046.954530410011941000.0000030.068983
310100350010104464.852310410011638000.0000040.066733
41010045001010104.833410410012541000.0000030.065343
510100550010101031.911980410011841000.0000030.067173
610100650010104424.34167041001538000.0000040.062143
710100750010101026.750330410012141000.0000030.066623
810100850010104463.664850410011738000.0000040.066493
910100950010100032.902170410011248000.0000010.079033
idTimeIs_CHwho_CHDist_To_CHADV_SADV_RJOIN_SJOIN_RSCH_SSCH_RRankDATA_SDATA_RData_Sent_To_BSdist_CH_To_BSsend_codeExpaned_EnergyAttack_type
3746512010911003020103736.4350405100163902382.3893320.070613
3746522010921003020106323.85398051001196038149.3991940.162733
3746532010931003020109518.812690510011355032124.7845910.094773
3746542010941003020100417.164000510011857043166.8939750.097223
374655201095100312010950.00000140211000112022124.2017001.018073
374656201096100302010516.98337051001796067170.1477930.159743
3746572010971003020103729.32867051001313902482.2104320.068773
3746582010981003020109518.519630510011755031139.2643810.094373
374659201099100302010518.55001051001396065158.2749230.160473
374660202041102502021000.00000050000476897115.0040701.013253